Drug-Induced Phenotypes
2020-06-28
Contents
1 Introduction
Drug effects are defined as the vector pointing from negative control (DMSO) organoids to treated organoids in feature space. Features are transformed with a PCA and the top 25 components are retained. An SVM with a linear kernel is trained to differentiate DMSO and treated organoids (for each drug individually) and the normal vector to the separating hyperplane is defined as the drug effect vector. It is scaled to be as long as the distance between the DMSO median and the treated organoid median in feature space.
The paper’s “story flow” began with the drug-induced phenotypes and then continued onto the organoid viability. However, there are interesting parallels between the two analyses so that I include the lethality of drugs in the annotation of heatmaps here.
2 Define Active Drugs
A histogram of the accuracies (or AUCs to be exact) shows that there are two groups of drugs. The first, normally distributed, group with low AUCs represents the inactive drugs that cannot be sufficiently differentiated from DMSO while the other group represents the active drugs that have significantly different characteristic features than the DMSO organoids and can be separated by a linear SVM hyperplane in feature space.
ggplot(data = drug_effect_metadata, mapping = aes(x = AUC_Mean)) +
geom_histogram(bins = 100) +
theme_vignette() + ggtitle("AUROC Histogram") +
xlab("Mean AUROC (10-fold Cross Validation)") + ylab("") +
if(save_images) ggsave(
filename = file.path(img_out_dir, "Histogram_ClassifierAUCs.pdf"),
width = 3, height = 3, useDingbats = FALSE)
The distance in feature space between drugs and DMSO clouds correlates, as expected, with the AUROC.
ggplot(data = drug_effect_metadata,
mapping = aes(x = AUC_Mean, y = Distance)) +
geom_point(size = 0.5) +
theme_vignette() + labs(
caption = paste("Spearman rho =", round(
cor(drug_effect_metadata$AUC_Mean,
drug_effect_metadata$Distance, method = "spearman"), 2))) +
xlab("AUROC") + labs(color = "")
if(save_images) ggsave(
filename = file.path(img_out_dir, "Scatterplot_AUCvsDistance.pdf"),
width = 3, height = 3, useDingbats = FALSE)The SVM was trained with 10-fold cross validation and the resulting means and standard devations of the accuracies are shown below.
ggplot(data = drug_effect_metadata,
mapping = aes(x = AUC_Mean, y = AUC_Std)) +
geom_point(size = 0.5) + theme_vignette() +
labs(x = "AUROC Mean", y = "AUROC Standard Deviation", color = "")
if(save_images) ggsave(
filename = file.path(img_out_dir, "Scatterplot_AUCMeanvsAUCStd.pdf"),
width = 3, height = 3, useDingbats = FALSE)Based on the bimodal histogram of active drugs, I define any drug that can be separated with \(AUC = 0.85\) from the DMSO controls as “active”.
auc_thresh = 0.85
profiles_active = drug_effect_profiles[drug_effect_metadata$AUC_Mean >= auc_thresh, ]
metadata_active = drug_effect_metadata[drug_effect_metadata$AUC_Mean >= auc_thresh, ]The number of active drugs varies per line.
ggplot_df = as.data.frame(table(metadata_active$Line))
# D054T01 was only imaged with the small library
ggplot_df = ggplot_df[ggplot_df$Var1 != "D054T01", ]
ggplot_df$TextLoc = ggplot_df$Freq - 50
ggplot(data = ggplot_df, mapping = aes(x = Var1, y = Freq)) +
geom_col() +
geom_text(mapping = aes(x = Var1, y = TextLoc, label = Freq),
color = "white", fontface = "bold", size = 3) +
theme_vignette() +
theme(axis.line = element_blank(), axis.text.x = element_blank(),
axis.ticks = element_blank()) +
xlab("") + ylab("") + ggtitle("Number of Active Drugs") +
coord_flip()
if(save_images) ggsave(
filename = file.path(img_out_dir, "Barplot_NumActiveDrugs.pdf"),
width = 2.5, height = 3)Some drugs are active in all cell lines and others only in very few.
activity_table = table(metadata_active$Drug, metadata_active$Line)
activity_table = activity_table[, colnames(activity_table) != "D054T01"]
ggplot_df = as.data.frame(sort(rowSums(activity_table > 0), decreasing = TRUE))
colnames(ggplot_df) = "Freq"
ggplot_df$Var1 = factor(rownames(ggplot_df), levels = rownames(ggplot_df))
ggplot(data = ggplot_df, mapping = aes(x = Var1, y = Freq)) +
geom_col(width = 1) + theme_vignette() + theme(
axis.text.x = element_blank(), axis.ticks.x = element_blank()) +
xlab("Drugs") + ylab("Number of Lines") +
ggtitle("Number of Lines in which a Drug is Active") +
scale_y_continuous(breaks = seq_len(length(lines)))
A comparison of the viability versus the drug effect shows that all drugs previously identified as lethal are also identified as active. However, a good number of active drugs were previously identified as nonlethal.
ggplot(mapping = aes(y = Viability, x = AUC_Mean)) +
geom_point(data = drug_effect_metadata, color = "lightgrey", size = 0.5) +
geom_point(data = drug_effect_metadata[
drug_effect_metadata$AUC_Mean >= auc_thresh, ],
size = 0.5) +
theme_vignette() + ylab("Viability") + xlab("AUROC Mean") +
geom_segment(mapping = aes(
y = 0, yend = 1, x = auc_thresh, xend = auc_thresh),
color = "red", size = 1) + coord_equal(ratio = 0.5)
if(save_images) ggsave(
filename = file.path(img_out_dir, "Scatterplot_ViabilityVsSVMAUROC.pdf"),
width = 3, height = 3, useDingbats = FALSE)Plotting a histogram of the viabilities of all “active” drugs, i.e. with \(AUROC \geq 0.85\)
ggplot(data = drug_effect_metadata[
drug_effect_metadata$AUC_Mean >= auc_thresh, ]) +
geom_histogram(mapping = aes(x = Viability), bins = 25) +
theme_vignette()
3 KiStem library
I analyze the two libraries separately and begin with the KiStem library.
sel_indices = (is.na(drug_effect_metadata$Concentration) | (
drug_effect_metadata$Drug %in% c("Bortezomib", "Irinotecan / SN-38") &
drug_effect_metadata$Concentration %in% c(0.2, 1))) &
# D054 was only treated with the CCP library
drug_effect_metadata$Line != "D054T01"
profiles = drug_effect_profiles[sel_indices, ]
metadata = drug_effect_metadata[sel_indices, ]sel_indices = metadata$AUC_Mean >= auc_thresh
profiles_active = profiles[sel_indices, ]
metadata_active = metadata[sel_indices, ]3.1 Individual Cell Lines
I look at the angles between vectors. The SVM generates a profile for all drugs, including inactive ones. However, because they cannot be properly separated from DMSO, the profiles of these drugs will be largely random, ergo I remove them as noise.
killing_anno = rep(NA, nrow(metadata_active))
killing_anno[metadata_active$Viability <= 0.2] = "0.2"
killing_anno[
metadata_active$Viability > 0.2 &
metadata_active$Viability <= 0.4] = "0.4"
killing_anno[
metadata_active$Viability > 0.4 &
metadata_active$Viability <= 0.6] = "0.6"
killing_anno[
metadata_active$Viability > 0.6 &
metadata_active$Viability <= 0.8] = "0.8"
killing_anno[
metadata_active$Viability > 0.8 &
metadata_active$Viability <= 1] = "1"
annotation = data.frame(
"Viability" = killing_anno,
"Pathway" = SCOPEAnalysis::get_mode_of_action(metadata_active$Drug),
"Target" = SCOPEAnalysis::get_targets(metadata_active$Drug),
"Line" = metadata_active$Line,
row.names = rownames(profiles_active),
stringsAsFactors = FALSE)
# Fix for slashes in targets
annotation$Target = gsub(
pattern = "/", replacement = ", ", annotation$Target)
# Calculate the angles for each line
line_results = list()
enriched_drugs = list()
oddsratio_pathway = list()
oddsratio_target = list()
for(line in unique(substr(rownames(annotation), 1, 7))) {
line_sel_indices = annotation$Line == line
line_annotation = annotation[line_sel_indices, ]
line_annotation$Line = NULL
line_profiles = profiles_active[line_sel_indices, ]
line_metadata = metadata_active[line_sel_indices, ]
# Calc angles and cluster
angles = get_angles(line_profiles)
if(nrow(angles) < 2) next
d = acos(angles)*180/pi
hc = hclust(as.dist(d), method = "ward.D2")
# Also cluster the principal components by correlation just for kicks
cor_pca = cor(line_profiles)
d_pca = as.dist((1 - cor_pca) / 2)
hc_pca = hclust(d_pca, method = "ward.D2")
# Annotate Pathway and Targets
pathways = list()
for(pathway in unique(line_annotation$Pathway)) {
pathways[[pathway]] = grep(
pattern = paste0("\\b", pathway, "\\b"),
x = line_annotation$Pathway,
ignore.case = TRUE)
}
# My cluster_enrichment function doesn't play well with NA values yet
pathways = pathways[!is.na(names(pathways))]
pathways_sig_vec = get_cluster_enrichment(
clustering = hc, labels = pathways, min_cluster_size = min_cluster_size,
max_annotated_labels = 7)
# 'Others' is too vague
pathways_sig_vec$Labels[pathways_sig_vec$Labels == "Others"] = NA
targets_split = tolower(line_annotation$Target)
targets = list()
for(target in unique(unlist(strsplit(targets_split, ", ")))) {
targets[[target]] = grep(
pattern = paste0("\\b", target, "\\b"),
x = targets_split, ignore.case = TRUE)
}
# My cluster_enrichment function doesn't play well with NA values yet
targets = targets[!is.na(names(targets))]
targets_sig_vec = get_cluster_enrichment(
clustering = hc, labels = targets, min_cluster_size = min_cluster_size,
max_annotated_labels = 7)
line_annotation$Pathway = pathways_sig_vec$Labels
line_annotation$Target = targets_sig_vec$Labels
enriched_drugs[[line]] = data.frame(
"Drug" = substr(rownames(line_annotation), 9, 100000L),
"Line" = line,
"Pathway" = annotation[annotation$Line == line, "Pathway"],
"Target" = annotation[annotation$Line == line, "Target"],
"Enriched.Target" = line_annotation$Target,
"is.pathway.enriched" = !is.na(line_annotation$Pathway),
"is.target.enriched" = !is.na(line_annotation$Target),
stringsAsFactors = FALSE)
if(nrow(pathways_sig_vec$Fisher.Test) > 0) {
oddsratio_pathway[[line]] = cbind.data.frame(
"Line" = line, pathways_sig_vec$Fisher.Test)
}
if(nrow(targets_sig_vec$Fisher.Test) > 0) {
oddsratio_target[[line]] = cbind.data.frame(
"Line" = line, targets_sig_vec$Fisher.Test)
}
line_results[[line]] = list(
"Annotation" = line_annotation,
"Distances" = d,
"Clustering" = hc,
"PCAClustering" = hc_pca)
}# Save enriched drugs
enriched_drugs = do.call(rbind, enriched_drugs)
rownames(enriched_drugs) = NULL
if(save_images) write.csv2(
x = enriched_drugs, file = "EnrichedDrugs.csv",
quote = FALSE, row.names = FALSE)
# Save oddsratio Ratios
oddsratio_pathway = do.call(rbind, oddsratio_pathway)
rownames(oddsratio_pathway) = NULL
if(save_images) write.csv2(
x = oddsratio_pathway, file = "OddsRatio_Pathways.csv",
quote = FALSE, row.names = FALSE)
oddsratio_target = do.call(rbind, oddsratio_target)
rownames(oddsratio_target) = NULL
if(save_images) write.csv2(
x = oddsratio_target, file = "OddsRatio_Target.csv",
quote = FALSE, row.names = FALSE)anno_colorScale = list(
"Viability" = setNames(
object = colorRampPalette(c("black", "white"))(
length(unique(annotation$Viability))),
nm = sort(unique(annotation$Viability))))
# Plot all results
for(line in names(line_results)) {
line_annotation = line_results[[line]]$Annotation
line_profiles = profiles_active[annotation$Line == line,]
line_color = anno_colorScale
if(sum(!is.na(line_annotation$Pathway)) == 0) line_annotation$Pathway = NULL
if(sum(!is.na(line_annotation$Target)) == 0) line_annotation$Target = NULL
for(column in colnames(line_annotation)) {
if(column %in% names(line_color)) next
entries = na.omit(unique(line_annotation[[column]]))
line_color[[column]] = setNames(
object = unname(colorScale)[seq_along(entries)],
nm = entries)
}
d = line_results[[line]]$Distances
hc = line_results[[line]]$Clustering
hc_pca = line_results[[line]]$PCAClustering
# Heatmap of PCA components
lp = line_profiles
lp[lp > 0] = sqrt(lp[lp > 0])
lp[lp < 0] = -sqrt(-lp[lp < 0])
hm_range_limit = max(abs(lp))
hm_breaks = seq(-hm_range_limit, hm_range_limit, length.out = 100)
hm_legend_breaks = c(-floor(hm_range_limit), 0, floor(hm_range_limit))
hm_legend_labels = hm_legend_breaks**2
hm_legend_labels[hm_legend_breaks < 0] = -hm_legend_labels[hm_legend_breaks < 0]
hm_colorscale = colorRampPalette(
c("#0082c8", "white", "#e6194b"))(length(hm_breaks))
pheatmap(
t(lp), annotation_col = line_annotation,
color = hm_colorscale, breaks = hm_breaks,
legend_breaks = hm_legend_breaks, legend_labels = hm_legend_labels,
annotation_colors = line_color, show_rownames = TRUE,
show_colnames = FALSE, cluster_rows = hc_pca, cluster_cols = hc,
main = sprintf("Angles Between Profile Vectors for %s", line),
border_color = NA, cellheight = 7, cellwidth = 2)
if(save_images) pheatmap(
t(lp), annotation_col = line_annotation,
color = hm_colorscale, breaks = hm_breaks,
legend_breaks = hm_legend_breaks, legend_labels = hm_legend_labels,
annotation_colors = line_color, show_rownames = TRUE,
show_colnames = FALSE, cluster_rows = hc_pca, cluster_cols = hc,
main = sprintf("Angles Between Profile Vectors for %s", line),
border_color = NA, cellheight = 7, cellwidth = 2, height = 10,
filename = file.path(img_out_dir, sprintf("EffectVectorHeatmap_PCA_%s.pdf", line)))
# Symmetrical Heatmap (save as regular image and as super large image with rownames)
hm_colorscale = colorRampPalette(
rev(c("#f7f7f7", "#f7f7f7", "#f7f7f7",
"#E0F3F8", "#91BFDB", "#4575B4")))(150)
pheatmap(
d, annotation_col = line_annotation,
color = hm_colorscale, annotation_colors = line_color,
show_rownames = FALSE, show_colnames = FALSE,
cluster_rows = hc, cluster_cols = hc,
main = sprintf("Angles Between Profile Vectors for %s", line),
border_color = NA, cellheight = 1.25, cellwidth = 1.25)
if(save_images) pheatmap(
d, annotation_col = line_annotation,
color = hm_colorscale, annotation_colors = line_color,
show_rownames = TRUE, show_colnames = FALSE,
cluster_rows = hc, cluster_cols = hc,
main = sprintf("Angles Between Profile Vectors for %s", line),
# border_color = NA, cellheight = 1.25, cellwidth = 1.25, height = 10,
border_color = NA, cellheight = 8, cellwidth = 8,
filename = file.path(img_out_dir, sprintf("EffectVectorHeatmap_superlarge_%s.pdf", line)))
if(save_images) pheatmap(
d, annotation_col = line_annotation,
color = hm_colorscale, annotation_colors = line_color,
show_rownames = FALSE, show_colnames = FALSE,
cluster_rows = hc, cluster_cols = hc,
main = sprintf("Angles Between Profile Vectors for %s", line),
border_color = NA, cellheight = 1.25, cellwidth = 1.25, height = 10,
filename = file.path(img_out_dir, sprintf("EffectVectorHeatmap_%s.pdf", line)))
# Force-directed graph of active drugs
# Set up adjacency matrix
adjmatrix = d
adjmatrix[adjmatrix > 45] = 0
# Resort matrix to make sure annotated nodes are above unannotated ones
sort_order = order(line_annotation$Target, na.last = FALSE)
adjmatrix = adjmatrix[sort_order, sort_order]
# Remove drugs without any connections
sel_indices = colSums(adjmatrix != 0) != 0
adjmatrix = adjmatrix[sel_indices, sel_indices]
# Generate network
adjmatrix = Matrix::Matrix(data = adjmatrix, sparse = TRUE)
net = graph_from_adjacency_matrix(
adjmatrix = adjmatrix, mode = "undirected", diag = FALSE, weighted = TRUE)
# Assign targets and colors
V(net)$target = line_annotation[match(
V(net)$name, rownames(line_annotation)), "Target"]
V(net)$color = V(net)$target
for(target in na.omit(unique(V(net)$target))) {
regex = paste0("\\b", target, "\\b")
V(net)$color = gsub(regex, line_color$Target[target], V(net)$color)}
V(net)$color[is.na(V(net)$color)] = "white"
# Assign size
V(net)$size = 5
V(net)[!is.na(V(net)$target)]$size = 7.5
# # Create plot
# e = get.edgelist(net, names = FALSE)
# l = qgraph.layout.fruchtermanreingold(
# edgelist = e, vcount = vcount(net),
# area=20*(vcount(net)^2), repulse.rad=(vcount(net)^3.1))
# # l = layout_with_graphopt(graph = net, niter = 1000, mass = 100)
# plot.igraph(net, vertex.label = NA, layout=l,
# edge.width = 0.5, edge.color = "lightgray",
# main = line)
# if(save_images) {
# pdf(file = file.path(img_out_dir, sprintf("EffectVectorNetwork_%s.pdf", line)),
# width = 7, height = 7, useDingbats = FALSE)
# plot.igraph(net, vertex.label = NA, layout=l,
# edge.width = 0.5, edge.color = "lightgray",
# main = line)
# dev.off()}
# # t-SNE of full profiles
# line_profiles_full = profiles[metadata$Line == line, ]
# tsne = Rtsne(X = line_profiles_full, perplexity = 50, max_iter = 1000)
# tsnedf = data.frame(
# "X" = tsne$Y[,1],
# "Y" = tsne$Y[,2],
# "isActive" = rownames(line_profiles_full) %in% rownames(line_profiles),
# "Viability" = line_annotation[rownames(line_profiles_full), "Viability"],
# "Pathway" = line_annotation[rownames(line_profiles_full), "Pathway"],
# "Target" = line_annotation[rownames(line_profiles_full), "Target"],
# row.names = rownames(line_profiles_full)
# )
# ggplot() +
# # Inactive drugs
# geom_point(data = tsnedf[!tsnedf$isActive, ],
# mapping = aes(x = X, y = Y),
# color = "lightgray") +
# # Active Drugs
# geom_point(data = tsnedf[tsnedf$isActive, ],
# mapping = aes(x = X, y = Y)) +
# scale_color_manual(values = line_color$Target) +
# # Enriched Drugs (Target)
# geom_point(data = tsnedf[!is.na(tsnedf$Target), ],
# mapping = aes(
# x = X, y = Y,
# color = tsnedf[!is.na(tsnedf$Target), "Target"])) +
# theme_vignette() + ggtitle(paste0("t-SNE of all drugs ", line)) +
# labs(color = "Target")
# if(save_images) ggsave(
# filename = file.path(img_out_dir, sprintf(
# "EffectVector_allDrugs_tSNE_%s.pdf", line)),
# width = 8, height = 8, useDingbats = FALSE)
#
# # t-SNE of active drugs
# tsne = Rtsne(X = line_profiles, perplexity = 20, max_iter = 1000)
# tsnedf = data.frame(
# "X" = tsne$Y[,1],
# "Y" = tsne$Y[,2],
# "Viability" = line_annotation[rownames(line_profiles), "Viability"],
# "Pathway" = line_annotation[rownames(line_profiles), "Pathway"],
# "Target" = line_annotation[rownames(line_profiles), "Target"],
# row.names = rownames(line_profiles)
# )
# ggplot() +
# geom_point(data = tsnedf, mapping = aes(x = X, y = Y)) +
# scale_color_manual(values = line_color$Target) +
# # Enriched Drugs (Target)
# geom_point(data = tsnedf[!is.na(tsnedf$Target), ],
# mapping = aes(
# x = X, y = Y,
# color = tsnedf[!is.na(tsnedf$Target), "Target"])) +
# theme_vignette() + ggtitle(paste0("t-SNE of active drugs ", line)) +
# labs(color = "Target")
# if(save_images) ggsave(
# filename = file.path(img_out_dir, sprintf(
# "EffectVector_activeDrugs_tSNE_%s.pdf", line)),
# width = 8, height = 8, useDingbats = FALSE)
}
3.1.1 Closer Look at D004T01
D004T01 has a notable non-lethal cluster associated with mTOR
line = "D004T01"
line_annotation = line_results[[line]]$Annotation
line_profiles = profiles_active[annotation$Line == line,]
line_color = anno_colorScale
if(sum(!is.na(line_annotation$Pathway)) == 0) line_annotation$Pathway = NULL
if(sum(!is.na(line_annotation$Target)) == 0) line_annotation$Target = NULL
for(column in colnames(line_annotation)) {
if(column %in% names(line_color)) next
entries = na.omit(unique(line_annotation[[column]]))
line_color[[column]] = setNames(
object = unname(colorScale)[seq_along(entries)],
nm = entries)
}
d = line_results[[line]]$Distances
hc = line_results[[line]]$Clustering
hc_pca = line_results[[line]]$PCAClustering
labels = cutree(tree = hc, k = 2)
mtor_label = unique(labels[which(line_annotation$Target == "mtor")])
if(length(mtor_label) != 1) stop(
"Critical Error with mTOR label for D004T01")
mtor_drugs = names(which(labels == mtor_label))
mtor_annotation = annotation[mtor_drugs, ]
mtor_annotation$Target = ifelse(
test = !is.na(line_annotation[mtor_drugs, "Target"]),
yes = line_annotation[mtor_drugs, "Target"],
no = mtor_annotation[mtor_drugs, "Target"])
mtor_annotation$Line = NULL
mtor_annoColor = anno_colorScale
if(sum(!is.na(mtor_annotation$Pathway)) == 0) mtor_annotation$Pathway = NULL
if(sum(!is.na(mtor_annotation$Target)) == 0) mtor_annotation$Target = NULL
for(column in colnames(mtor_annotation)) {
if(column %in% names(mtor_annoColor)) next
entries = na.omit(unique(mtor_annotation[[column]]))
mtor_annoColor[[column]] = setNames(
object = unname(colorScale)[seq_along(entries)],
nm = entries)
}
d_mtor = d[labels == mtor_label, labels == mtor_label]
hc_mtor = hclust(as.dist(d_mtor), "ward.D2")
hm_colorscale = colorRampPalette(
rev(c("#f7f7f7", "#E0F3F8", "#91BFDB", "#4575B4")))(150)
drugnames = substr(mtor_drugs, 9, 100)
pheatmap(
mat = d_mtor, color = hm_colorscale,
annotation_col = mtor_annotation, annotation_colors = mtor_annoColor,
cluster_rows = hc_mtor, cluster_cols = hc_mtor, labels_col = drugnames,
show_rownames = FALSE, cellwidth = 10, cellheight = 10, border_color = NA,
height = 5)
if(save_images) pheatmap(
mat = d_mtor, color = hm_colorscale,
annotation_col = mtor_annotation, annotation_colors = mtor_annoColor,
cluster_rows = hc_mtor, cluster_cols = hc_mtor, labels_col = drugnames,
show_rownames = FALSE, cellwidth = 10, cellheight = 10, border_color = NA,
height = 10, filename = file.path(img_out_dir, sprintf(
"EffectVectorHeatmap_Detailed_%s.pdf", line)))3.2 Enrichment Heatmap
I look at enrichment heatmaps, i.e. which cell lines have an enrichment in which pathways / targets.
3.2.1 Log-Odds Heatmap
I create a heatmap of the enrichment (log odds ratio) to determine which pathways and targets are enriched in each cell line
heatmap_pathways = oddsratio_pathway[, c("Line", "Label", "OddsRatio")]
heatmap_pathways = acast(
data = heatmap_pathways,
formula = Line ~ Label, value.var = "OddsRatio",
fun.aggregate = max, fill = 0)
# Replace Inf with the finite maximum
heatmap_pathways[is.infinite(heatmap_pathways)] = max(
heatmap_pathways[!is.infinite(heatmap_pathways)])
heatmap_pathways[heatmap_pathways != 0] = log2(
heatmap_pathways[heatmap_pathways != 0])
hc_pathways = as.dist((1 - cor(heatmap_pathways)) / 2)
hc_pathways_line = as.dist((1 - cor(t(heatmap_pathways))) / 2)
heatmap_targets = oddsratio_target[, c("Line", "Label", "OddsRatio")]
heatmap_targets = acast(
data = heatmap_targets,
formula = Line ~ Label, value.var = "OddsRatio",
fun.aggregate = max, fill = 0)
# Replace Inf with the finite maximum
heatmap_targets[is.infinite(heatmap_targets)] = max(
heatmap_targets[!is.infinite(heatmap_targets)])
heatmap_targets[heatmap_targets != 0] = log2(
heatmap_targets[heatmap_targets != 0])
hc_targets = as.dist((1 - cor(heatmap_targets)) / 2)
hc_targets_line = as.dist((1 - cor(t(heatmap_targets))) / 2)
# hm_colorscale = colorRampPalette(
# c('#b2182b','#d6604d','#f4a582', '#f7f7f7','#f7f7f7', '#f7f7f7',
# '#f7f7f7', '#f7f7f7', '#f7f7f7'))(150)
hm_breaks = seq(0, max(heatmap_pathways, na.rm = TRUE), length.out = 50)
hm_colorscale = colorRampPalette(c("#f7f7f7", "#4575B4"))(length(hm_breaks))
pheatmap(
heatmap_pathways, cluster_rows = hc_pathways_line,
cluster_cols = hc_pathways,
breaks = hm_breaks, color = hm_colorscale,
cellwidth = 10, cellheight = 10)
if(save_images) {
pheatmap(
heatmap_pathways, cluster_rows = hc_pathways_line,
cluster_cols = hc_pathways,
breaks = hm_breaks, color = hm_colorscale,
cellwidth = 10, cellheight = 10,
filename = file.path(img_out_dir, "Heatmap_LogOdds_Pathways.pdf"))
}
hm_breaks = seq(0, max(heatmap_targets, na.rm = TRUE), length.out = 50)
hm_colorscale = colorRampPalette(c("#f7f7f7", "#4575B4"))(length(hm_breaks))
pheatmap(
heatmap_targets, cluster_rows = hc_targets_line,
cluster_cols = hc_targets,
breaks = hm_breaks, color = hm_colorscale,
cellwidth = 10, cellheight = 10)
if(save_images) {
pheatmap(
heatmap_targets, cluster_rows = hc_targets_line,
cluster_cols = hc_targets,
breaks = hm_breaks, color = hm_colorscale,
cellwidth = 10, cellheight = 10,
filename = file.path(img_out_dir, "Heatmap_LogOdds_Targets.pdf"))
}3.2.2 Binary Heatmap
binary_heatmap_pathways = heatmap_pathways
binary_heatmap_pathways[binary_heatmap_pathways > 0] = 1
binary_heatmap_targets = heatmap_targets
binary_heatmap_targets[binary_heatmap_targets > 0] = 1
hm_colorscale = colorRampPalette(c("#f7f7f7", "#4575B4"))(2)
pheatmap(
binary_heatmap_pathways, cluster_rows = hc_pathways_line,
cluster_cols = hc_pathways,
color = hm_colorscale, legend = FALSE,
cellwidth = 10, cellheight = 10)
pheatmap(
binary_heatmap_targets, cluster_rows = hc_targets_line,
cluster_cols = hc_targets,
color = hm_colorscale, legend = FALSE,
cellwidth = 10, cellheight = 10)
if(save_images) {
pheatmap(
binary_heatmap_pathways, cluster_rows = hc_pathways_line,
cluster_cols = hc_pathways,
color = hm_colorscale, legend = FALSE,
cellwidth = 10, cellheight = 10,
filename = file.path(img_out_dir, "Heatmap_Binary_Pathways.pdf"))
pheatmap(
binary_heatmap_targets, cluster_rows = hc_targets_line,
cluster_cols = hc_targets,
color = hm_colorscale, legend = FALSE,
cellwidth = 10, cellheight = 10,
filename = file.path(img_out_dir, "Heatmap_Binary_Targets.pdf"))
}3.3 Feature Differences
I look at the vectors from median DMSO to median drug features for each drug per replicate. I use the well-averaged features to improve loading times. For each line, I aggregate all drugs belonging to the enriched clusters.
# Extract only key features
key_features = c(
"x.0.s.area_expected" = "Area",
"x.0.m.eccentricity_expected" = "Eccentricity",
"x.a.b.q05_expected" = "Median Actin Intensity",
"x.b.b.q05_expected" = "Median FITC Intensity",
"x.c.b.q05_expected" = "Median DAPI Intensity")
# Set up color scale
pheno_color_scale = c(
"Median Actin Intensity" = "#e6194b",
"Median FITC Intensity" = "#3cb44b",
"Median DAPI Intensity" = "#0082c8",
"Area" = "#ffe119",
"Eccentricity" = "#f58231")
# Load features
data("well_features", package = "SCOPEAnalysis")
well_features = well_features[, names(key_features)]
# Z-Scale features for each line and replicate
for(line in unique(well_metadata$Line)) {
for(r in unique(well_metadata$Replicate)) {
rep_sel = well_metadata$Line == line & well_metadata$Replicate == r
well_features[rep_sel, ] = apply(
X = well_features[rep_sel, ],
MARGIN = 2, FUN = function(x) (x - median(x)) / mad(x))
}
}
# Subtract DMSO medians for each line and replicate
for(line in unique(well_metadata$Line)) {
for(r in unique(well_metadata$Replicate)) {
rep_sel = well_metadata$Line == line & well_metadata$Replicate == r
rep_line_dmso_median = apply(
X = well_features[rep_sel & well_metadata$Drug == "DMSO", ],
MARGIN = 2, FUN = median, na.rm = TRUE)
well_features[rep_sel, ] = sweep(
x = well_features[rep_sel, ],
MARGIN = 2, STATS = rep_line_dmso_median, FUN = "-")
}
}
# Aggregate features for pathways for each line and replicate
all_pathway_features = list()
all_target_features = list()
for(line in unique(well_metadata$Line)) {
line_enriched_drugs = enriched_drugs[enriched_drugs$Line == line, ]
line_enriched_pathways = unique(line_enriched_drugs[
line_enriched_drugs$is.pathway.enriched, "Pathway"])
line_enriched_targets = unique(line_enriched_drugs[
line_enriched_drugs$is.target.enriched, "Enriched.Target"])
# Pathways
for(pathway in line_enriched_pathways) {
drugs = line_enriched_drugs[
line_enriched_drugs$Pathway == pathway &
line_enriched_drugs$is.pathway.enriched, ]
sel_indices = well_metadata$Drug %in% drugs$Drug & well_metadata$Line == line
relevant_features = well_features[sel_indices, ]
relevant_metadata = well_metadata[sel_indices, ]
# Aggregate
pathway_features = aggregate(
x = relevant_features,
by = list("Replicate" = relevant_metadata$Replicate),
FUN = median)
pathway_features$Line = line
pathway_features$Label = pathway
all_pathway_features[[length(all_pathway_features) + 1]] = pathway_features
}
# Targets
for(target in line_enriched_targets) {
drugs = line_enriched_drugs[
line_enriched_drugs$Enriched.Target == target &
line_enriched_drugs$is.target.enriched, ]
sel_indices = well_metadata$Drug %in% drugs$Drug & well_metadata$Line == line
relevant_features = well_features[sel_indices, ]
relevant_metadata = well_metadata[sel_indices, ]
# Aggregate
target_features = aggregate(
x = relevant_features,
by = list("Replicate" = relevant_metadata$Replicate),
FUN = median)
target_features$Line = line
target_features$Label = target
all_target_features[[length(all_target_features) + 1]] = target_features
}
}
all_pathway_features = do.call(rbind, all_pathway_features)
all_target_features = do.call(rbind, all_target_features)3.3.1 Phenoprints for Pathways
for(line in unique(all_pathway_features$Line)) {
labels = unique(all_pathway_features[
all_pathway_features$Line == line, "Label"])
for(label in labels) {
ggplot_df = all_pathway_features[all_pathway_features$Line == line &
all_pathway_features$Label == label, ]
ggplot_df$Line = NULL
ggplot_df$Label = NULL
ggplot_df = melt(ggplot_df, id.vars = c("Replicate"))
ggplot_df$variable = key_features[ggplot_df$variable]
ggplot_df$variable = factor(ggplot_df$variable, levels = key_features)
ggplot_df_bar = aggregate(
x = ggplot_df$value,
by = list("variable" = ggplot_df$variable),
FUN = median)
colnames(ggplot_df_bar) = c("variable", "value")
gp = ggplot(mapping = aes(x = variable, y = value)) +
geom_col(data = ggplot_df_bar, mapping = aes(fill = variable)) +
geom_point(data = ggplot_df) +
scale_fill_manual(values = pheno_color_scale) +
theme_vignette() + coord_fixed() +
theme(
axis.text.x = element_blank(), axis.line.x = element_blank(),
axis.ticks.x = element_blank(), legend.title = element_blank()) +
xlab("") + ylab("") + ggtitle(paste0(line, " ", label))
plot(gp)
if(save_images) ggsave(
filename = file.path(img_out_dir, sprintf(
"Phenoprints_Pathways_%s_%s.pdf", line, make.names(label))),
width = 5, height = 5, useDingbats = FALSE)
}
}
3.3.2 Phenoprints for Targets
for(line in unique(all_target_features$Line)) {
labels = unique(all_target_features[
all_target_features$Line == line, "Label"])
for(label in labels) {
ggplot_df = all_target_features[all_target_features$Line == line &
all_target_features$Label == label, ]
ggplot_df$Line = NULL
ggplot_df$Label = NULL
ggplot_df = melt(ggplot_df, id.vars = c("Replicate"))
ggplot_df$variable = key_features[ggplot_df$variable]
ggplot_df$variable = factor(ggplot_df$variable, levels = key_features)
ggplot_df_bar = aggregate(
x = ggplot_df$value,
by = list("variable" = ggplot_df$variable),
FUN = median)
colnames(ggplot_df_bar) = c("variable", "value")
gp = ggplot(mapping = aes(x = variable, y = value)) +
geom_col(data = ggplot_df_bar, mapping = aes(fill = variable)) +
geom_point(data = ggplot_df) +
scale_fill_manual(values = pheno_color_scale) +
theme_vignette() + coord_fixed() +
theme(
axis.text.x = element_blank(), axis.line.x = element_blank(),
axis.ticks.x = element_blank(), legend.title = element_blank()) +
xlab("") + ylab("") + ggtitle(paste0(line, " ", label))
plot(gp)
if(save_images) ggsave(
filename = file.path(img_out_dir, sprintf(
"Phenoprints_Targets_%s_%s.pdf", line, make.names(label))),
width = 5, height = 5, useDingbats = FALSE)
}
}
3.4 Drug Effect Differences across all Lines
3.4.1 Comparing Drug Effects Directly
Treating effect vectors of all drugs and all lines equally shows that drug effects on different lines are difficult to compare to each other. Line heterogeneity appears to overpower the effects of drugs. Even the drugs classified as lethal do not cluster together. Notable is that the lines D046T01 and D055T01, which look extremely similar on visual inspection, DO overlap.
angles = get_angles(profiles_active)
d = acos(angles)*180/pi
hc = hclust(as.dist(d), method = "ward.D2")
cor_pca = cor(profiles_active)
d_pca = as.dist((1 - cor_pca) / 2)
hc_pca = hclust(d_pca, method = "ward.D2")
killing_anno = rep(NA, nrow(metadata_active))
killing_anno[metadata_active$Viability <= 0.2] = "0.2"
killing_anno[
metadata_active$Viability > 0.2 &
metadata_active$Viability <= 0.4] = "0.4"
killing_anno[
metadata_active$Viability > 0.4 &
metadata_active$Viability <= 0.6] = "0.6"
killing_anno[
metadata_active$Viability > 0.6 &
metadata_active$Viability <= 0.8] = "0.8"
killing_anno[
metadata_active$Viability > 0.8 &
metadata_active$Viability <= 1] = "1"
annotation = data.frame(
"Viability" = killing_anno,
"Pathway" = get_mode_of_action(metadata_active$Drug),
"Target" = get_targets(metadata_active$Drug),
"Line" = metadata_active$Line,
row.names = rownames(profiles_active),
stringsAsFactors = FALSE)
# Annotate Pathway and Targets
pathways = list()
for(pathway in unique(annotation$Pathway)) {
pathways[[pathway]] = grep(
pattern = paste0("\\b", pathway, "\\b"),
x = annotation$Pathway,
ignore.case = TRUE)
}
# My cluster_enrichment function doesn't play well with NA values yet
pathways = pathways[!is.na(names(pathways))]
pathways_sig_vec = get_cluster_enrichment(
clustering = hc, labels = pathways, min_cluster_size = min_cluster_size)
# 'Others' is too vague
pathways_sig_vec$Labels[pathways_sig_vec$Labels == "Others"] = NA
targets_split = tolower(annotation$Target)
targets = list()
for(target in unique(unlist(strsplit(targets_split, ", ")))) {
targets[[target]] = grep(
pattern = paste0("\\b", target, "\\b"),
x = targets_split, ignore.case = TRUE)
}
# My cluster_enrichment function doesn't play well with NA values yet
targets = targets[!is.na(names(targets))]
targets_sig_vec = get_cluster_enrichment(
clustering = hc, labels = targets, min_cluster_size = min_cluster_size)
annotation$Pathway = pathways_sig_vec$Labels
annotation$Target = targets_sig_vec$Labels
# Set up colors
anno_colorScale = list(
"Viability" = setNames(
object = colorRampPalette(c("black", "white"))(
length(unique(annotation$Viability))),
nm = sort(unique(annotation$Viability))))
if(sum(!is.na(annotation$Pathway)) == 0) annotation$Pathway = NULL
if(sum(!is.na(annotation$Target)) == 0) annotation$Target = NULL
for(column in colnames(annotation)) {
if(column %in% names(anno_colorScale)) next
entries = na.omit(unique(annotation[[column]]))
anno_colorScale[[column]] = setNames(
object = unname(colorScale)[seq_along(entries)],
nm = entries)
}
# Set up heatmap color scale
sqrt_profiles = profiles_active
sqrt_profiles[sqrt_profiles > 0] = sqrt(sqrt_profiles[sqrt_profiles > 0])
sqrt_profiles[sqrt_profiles < 0] = -sqrt(-sqrt_profiles[sqrt_profiles < 0])
hm_range_limit = max(abs(sqrt_profiles))
hm_breaks = seq(-hm_range_limit, hm_range_limit, length.out = 100)
hm_legend_breaks = c(-floor(hm_range_limit), 0, floor(hm_range_limit))
hm_legend_labels = hm_legend_breaks**2
hm_legend_labels[hm_legend_breaks < 0] = -hm_legend_labels[hm_legend_breaks < 0]
hm_colorscale = colorRampPalette(
c("#0082c8", "white", "#e6194b"))(length(hm_breaks))
pheatmap(
mat = t(profiles_active), cluster_rows = hc_pca, cluster_cols = hc,
show_colnames = FALSE, annotation_col = annotation, breaks = hm_breaks,
legend_breaks = hm_legend_breaks, legend_labels = hm_legend_labels,
color = hm_colorscale, annotation_colors = anno_colorScale,
cellwidth = 0.1, cellheight = 7)
if(save_images) pheatmap(
mat = t(profiles_active), cluster_rows = hc_pca, cluster_cols = hc,
show_colnames = FALSE, annotation_col = annotation, breaks = hm_breaks,
legend_breaks = hm_legend_breaks, legend_labels = hm_legend_labels,
color = hm_colorscale, annotation_colors = anno_colorScale,
cellwidth = 0.2, cellheight = 7, width = 10, height = 10,
filename = file.path(
img_out_dir, "EffectVectorHeatmap_allDrugs_noConcat_PCA.pdf"))
hm_colorscale = colorRampPalette(
rev(c("#f7f7f7", "#f7f7f7", "#f7f7f7",
"#E0F3F8", "#91BFDB", "#4575B4")))(150)
pheatmap(
mat = d, cluster_rows = hc, cluster_cols = hc, color = hm_colorscale,
show_colnames = FALSE, show_rownames = FALSE,
annotation_col = annotation, annotation_colors = anno_colorScale,
cellwidth = 0.1, cellheight = 0.1)
if(save_images) pheatmap(
mat = d, cluster_rows = hc, cluster_cols = hc, color = hm_colorscale,
show_colnames = FALSE, show_rownames = FALSE,
annotation_col = annotation, annotation_colors = anno_colorScale,
cellwidth = 0.1, cellheight = 0.1, height = 10,
filename = file.path(
img_out_dir, "EffectVectorHeatmap_allDrugs_noConcat.pdf"))There is one notably enriched cluster of mTOR inhibitors, in particular the drug “AZD2014” is enriched in 11 cell lines, as well several times in the pan-cell line clustering.
sel_indices = metadata_active$Drug == "AZD2014"
drug_profiles = profiles_active[sel_indices, ]
drug_annotation = annotation[sel_indices, ]
angles_drug = get_angles(drug_profiles)
d_drug = acos(angles_drug) * 180 / pi
hc_drug = hclust(as.dist(d_drug), method = "ward.D2")
hm_colorscale = colorRampPalette(
rev(c("#f7f7f7", "#E0F3F8", "#91BFDB", "#4575B4")))(150)
pheatmap(
mat = d_drug, cluster_rows = hc_drug, cluster_cols = hc_drug,
color = hm_colorscale, labels_row = drug_annotation$Line,
show_colnames = FALSE)
3.4.2 Concatenating Vectors
Instead of treating each drug per line separately, I concatenate the effect vectors on all lines for each drug. The resulting composite vector describes the drug’s effect on all cell lines without attempting to compare cell lines.
profiles_concat = list()
for(drug in sort(unique(metadata_active$Drug))) {
# for(drug in sort(unique(metadata$Drug))) {
profiles_concat_drug = c()
for(line in sort(unique(metadata_active$Line))) {
# for(line in sort(unique(metadata$Line))) {
tmp = profiles[
metadata$Drug == drug &
metadata$Line == line, , drop = FALSE]
if(is.null(nrow(tmp))) stop("Critical Error: the code expects a matrix")
if(nrow(tmp) == 0) {
tmp = matrix(
data = NA, nrow = 1, ncol = n_components,
dimnames = list(drug, paste0(line, ".", colnames(tmp))))
} else if(nrow(tmp) > 1) {
tmp = matrix(
colMeans(tmp), nrow=1,
dimnames = list(drug, paste0(line, ".", colnames(tmp))))
} else {
rownames(tmp) = drug
colnames(tmp) = paste0(line, ".", colnames(tmp))
}
profiles_concat_drug = cbind(profiles_concat_drug, as.matrix(tmp))
}
profiles_concat[[drug]] = data.frame(
profiles_concat_drug)
}
profiles_concat = do.call(rbind, profiles_concat)
# Impute missing values with the means for the corresponding PC of all other
# lines for that drug
missing_drugs = which(is.na(profiles_concat), arr.ind = T)
for(ii in seq_len(nrow(missing_drugs))) {
entry = missing_drugs[ii, ]
pc = strsplit(colnames(profiles_concat)[entry[2]], ".", fixed = TRUE)[[1]][2]
other_pc_cols = sapply(
strsplit(colnames(profiles_concat), ".", fixed = TRUE),
"[[", 2) == pc
mean_val = mean(as.numeric(
profiles_concat[entry[1], other_pc_cols]), na.rm = TRUE)
profiles_concat[entry[1], entry[2]] = mean_val
}
# If a drug has no effect in a line, I set its respective PC values to 0
# for(drug in rownames(profiles_concat)) {
# for(line in sort(unique(metadata_active$Line))) {
# drug_is_active = metadata$AUC_Mean[
# metadata$Line == line &
# metadata$Drug == drug] >= auc_thresh
# if(length(drug_is_active) == 0) next
# if(!drug_is_active) profiles_concat[
# drug, substr(colnames(profiles_concat), 1, 7) == line] = 0
# }
# }
# Cluster
angles = get_angles(profiles_concat)
d = acos(angles) * 180 / pi
hc = hclust(as.dist(d), method = "ward.D2")
annotation = data.frame(
"Pathway" = get_mode_of_action(rownames(profiles_concat)),
"Target" = get_targets(rownames(profiles_concat)),
row.names = rownames(profiles_concat),
stringsAsFactors = FALSE)
# Annotate Pathway and Targets
pathways = list()
for(pathway in unique(annotation$Pathway)) {
pathways[[pathway]] = grep(
pattern = paste0("\\b", pathway, "\\b"),
x = annotation$Pathway,
ignore.case = TRUE)
}
pathways_sig_vec = get_cluster_enrichment(
clustering = hc, labels = pathways, min_cluster_size = min_cluster_size)
# 'Others' is too vague
pathways_sig_vec$Labels[pathways_sig_vec$Labels == "Others"] = NA
targets_split = tolower(annotation$Target)
targets = list()
for(target in unique(unlist(strsplit(targets_split, ", ")))) {
targets[[target]] = grep(
pattern = paste0("\\b", target, "\\b"),
x = targets_split, ignore.case = TRUE)
}
targets_sig_vec = get_cluster_enrichment(
clustering = hc, labels = targets, min_cluster_size = min_cluster_size)
annotation$Pathway = pathways_sig_vec$Labels
annotation$Target = targets_sig_vec$Labels
if(sum(!is.na(annotation$Pathway)) == 0) annotation$Pathway = NULL
if(sum(!is.na(annotation$Target)) == 0) annotation$Target = NULL
# Add a summary statistic for lethality
lethality_degree = aggregate(
x = metadata$Viability <= 0.25,
by = list("Drug" = metadata$Drug),
FUN = sum)
rownames(lethality_degree) = lethality_degree$Drug
lethality_degree$Drug = NULL
# Hack to normalize number of lethal lines for positive controls
lethality_degree$x[lethality_degree$x > length(unique(metadata$Line))] = length(unique(metadata$Line))
annotation$Lethality = lethality_degree[rownames(annotation), "x"]
# Set up colors
anno_colorScale = list()
for(column in colnames(annotation)) {
if(column %in% names(anno_colorScale)) next
entries = sort(na.omit(unique(annotation[[column]])))
anno_colorScale[[column]] = setNames(
object = unname(colorScale)[seq_along(entries)],
nm = entries)
}
anno_colorScale$Lethality = colorRampPalette(
c("white", "black"))(length(anno_colorScale$Lethality))
# hm_colorscale = colorRampPalette(
# rev(c("#f7f7f7", "#f7f7f7", "#f7f7f7", "#f7f7f7",
# "#E0F3F8", "#91BFDB", "#4575B4")))(150)
hm_colorscale = colorRampPalette(
rev(c("#f7f7f7", "#E0F3F8", "#91BFDB", "#4575B4")))(150)
anno_colorScale$Target = setNames(
object = c("#e6194b", "#3cb44b", "#ffe119", "#0082c8", "#f58231",
"#911eb4", "#46f0f0", "#f032e6", "#d2f53c", "#fabebe",
"#008080", "#e6beff", "#aa6e28", "#fffac8", "#800000",
"#aaffc3", "#808000", "#ffd8b1", "#000080", "#808080",
"#d0d0d0", "#000000"),
nm = names(anno_colorScale$Target))
pheatmap(
mat = d, annotation_col = annotation,
color = hm_colorscale, annotation_colors = anno_colorScale,
show_rownames = FALSE, show_colnames = FALSE,
cluster_rows = hc, cluster_cols = hc,
main = "Angles Between Profile Vectors for All Lines",
border_color = NA)
if(save_images) pheatmap(
d, annotation_col = annotation,
color = hm_colorscale, annotation_colors = anno_colorScale,
show_rownames = FALSE, show_colnames = FALSE,
cluster_rows = hc, cluster_cols = hc,
main = sprintf("Angles Between Profile Vectors for all lines"),
border_color = NA, cellwidth = 1.5, cellheight = 1.5, height = 15,
filename = file.path(img_out_dir, "EffectVectorHeatmap_allDrugs.pdf"))Shown are the individual drug viabilities for each line according to the clustering for the drug effects to further showcase the compatibility between the methods
active_drugs = rownames(d)
active_viabilities = metadata[metadata$Drug %in% active_drugs, ]
# Aggregate drugs over wells
active_viabilities = aggregate(
x = active_viabilities$Viability,
by = list("Drug" = active_viabilities$Drug,
"Line" = active_viabilities$Line),
FUN = median, na.rm = TRUE)
active_viabilities = acast(
data = active_viabilities,
formula = Line ~ Drug, value.var = "x")
# Remove viability from annotation
anno_noLethality = annotation
anno_noLethality$Lethality = NULL
anno_colorScale$Lethality = NULL
pheatmap(
mat = active_viabilities, annotation_col = anno_noLethality,
color = hm_colorscale, annotation_colors = anno_colorScale,
show_rownames = TRUE, show_colnames = FALSE,
cluster_rows = TRUE, cluster_cols = hc,
main = "Induced Viabilities of Drugs",
border_color = NA)
if(save_images) pheatmap(
mat = active_viabilities, annotation_col = anno_noLethality,
color = hm_colorscale, annotation_colors = anno_colorScale,
show_rownames = TRUE, show_colnames = FALSE,
cluster_rows = TRUE, cluster_cols = hc,
main = "Induced Viabilities of Drugs",
border_color = NA, cellwidth = 1.5, cellheight = 10, height = 15,
filename = file.path(img_out_dir, "EffectVectorHeatmap_Viabilities_allDrugs.pdf"))Generate a list of column/row names with annotations
annotation_table = annotation[hc$labels[hc$order], ]
colnames(annotation_table) = c(
"EnrichedPathway", "EnrichedTarget", "Lethality")
annotation_table$Pathway = get_mode_of_action(rownames(annotation_table))
annotation_table$Targets = tolower(get_targets(rownames(annotation_table)))
annotation_table = annotation_table[, c(
"Pathway", "Targets", "EnrichedPathway", "EnrichedTarget", "Lethality")]
print(annotation_table)## Pathway
## Ulixertinib (BVD-523, VRT752271) MAPK
## TAK-733 MAPK
## Trametinib (GSK1120212) MAPK
## Cobimetinib (GDC-0973, RG7420) MAPK
## MEK162 (ARRY-162, ARRY-438162) MAPK
## PD0325901 DNA Damage
## Refametinib (RDEA119, Bay 86-9766) Others
## AZD8330 MAPK
## Pimasertib (AS-703026) MAPK
## RAF265 (CHIR-265) MAPK
## Selumetinib (AZD6244) MAPK
## PD318088 MAPK
## VX-11e MAPK
## NVP-BHG712 Protein Tyrosine Kinase
## YM201636 PI3K/Akt/mTOR
## Lapatinib (GW-572016) Ditosylate Protein Tyrosine Kinase
## PF-573228 Angiogenesis
## BMS-265246 Cell Cycle
## KU-60019 DNA Damage
## Triciribine Others
## CUDC-101 Epigenetics
## GSK461364 Cell Cycle
## AZD7762 Cell Cycle
## Rigosertib (ON-01910) Cell Cycle
## BI 2536 Others
## JNJ-7706621 Cell Cycle
## PIK-93 PI3K/Akt/mTOR
## PF-4708671 PI3K/Akt/mTOR
## Flavopiridol (Alvocidib) Cell Cycle
## AT7519 Cell Cycle
## SNS-032 (BMS-387032) Others
## Staurosporine_500nM <NA>
## BMS-754807 Others
## LY3023414 PI3K/Akt/mTOR
## Hesperadin Cell Cycle
## KW-2449 Angiogenesis
## Crizotinib (PF-02341066) Others
## ENMD-2076 Angiogenesis
## HMN-214 Cell Cycle
## BKM120 (NVP-BKM120, Buparlisib) PI3K/Akt/mTOR
## NVP-AEW541 Protein Tyrosine Kinase
## Alisertib (MLN8237) Others
## GSK2292767 PI3K/Akt/mTOR
## BGT226 (NVP-BGT226) PI3K/Akt/mTOR
## Bortezomib <NA>
## CUDC-907 Cytoskeletal Signaling
## CHIR-124 Cell Cycle
## Pacritinib (SB1518) JAK/STAT
## AT9283 Others
## Irinotecan / SN-38 <NA>
## JNK Inhibitor IX MAPK
## Tivantinib (ARQ 197) Protein Tyrosine Kinase
## HS-173 PI3K/Akt/mTOR
## Wnt agonist 1 Stem Cells & Wnt
## FRAX597 Cytoskeletal Signaling
## Tepotinib (EMD 1214063) Protein Tyrosine Kinase
## Golvatinib (E7050) Protein Tyrosine Kinase
## KX2-391 Angiogenesis
## VE-822 PI3K/Akt/mTOR
## AZ20 PI3K/Akt/mTOR
## AZD6738 PI3K/Akt/mTOR
## AMG-900 Cell Cycle
## PF-543 GPCR & G Protein
## PF-3758309 Cytoskeletal Signaling
## AZD5438 Cell Cycle
## R547 Cell Cycle
## A-674563 PI3K/Akt/mTOR
## Dinaciclib (SCH727965) Cell Cycle
## P276-00 Cell Cycle
## Flavopiridol HCl Cell Cycle
## PHA-767491 Cell Cycle
## PF-00562271 Angiogenesis
## PF-562271 Angiogenesis
## IKK-16 (IKK Inhibitor VII) NF-_B
## LDK378 Protein Tyrosine Kinase
## Vacquinol-1 MAPK
## GSK2126458 (GSK458) PI3K/Akt/mTOR
## Torin 2 PI3K/Akt/mTOR
## AZD3463 Protein Tyrosine Kinase
## AP26113 Protein Tyrosine Kinase
## SC1 MAPK
## SGI-7079 Protein Tyrosine Kinase
## PF-431396 Angiogenesis
## TAE226 (NVP-TAE226) Angiogenesis
## Milciclib (PHA-848125) Cell Cycle
## TG101209 JAK/STAT
## TG101348 (SAR302503) JAK/STAT
## GSK690693 Others
## GDC-0068 PI3K/Akt/mTOR
## MK-2206 2HCl Others
## AZD5363 PI3K/Akt/mTOR
## Uprosertib (GSK2141795) PI3K/Akt/mTOR
## BMS-536924 Protein Tyrosine Kinase
## OSI-906 (Linsitinib) Others
## ZSTK474 Neuronal Signaling
## KU-0063794 PI3K/Akt/mTOR
## WYE-354 PI3K/Akt/mTOR
## Temsirolimus (CCI-779, NSC 683864) Neuronal Signaling
## Everolimus (RAD001) Others
## Ridaforolimus (Deforolimus, MK-8669) PI3K/Akt/mTOR
## GDC-0980 (RG7422) PI3K/Akt/mTOR
## INK 128 (MLN0128) PI3K/Akt/mTOR
## VS-5584 (SB2343) PI3K/Akt/mTOR
## PF-04691502 PI3K/Akt/mTOR
## PP121 Protein Tyrosine Kinase
## CC-223 PI3K/Akt/mTOR
## PP242 PI3K/Akt/mTOR
## AZD2014 PI3K/Akt/mTOR
## GDC-0349 PI3K/Akt/mTOR
## OSI-027 PI3K/Akt/mTOR
## AZD8055 PI3K/Akt/mTOR
## CH5132799 PI3K/Akt/mTOR
## WYE-125132 (WYE-132) PI3K/Akt/mTOR
## CO-1686 (AVL-301) Protein Tyrosine Kinase
## A66 PI3K/Akt/mTOR
## BYL719 PI3K/Akt/mTOR
## Akti-1/2 PI3K/Akt/mTOR
## WAY-600 PI3K/Akt/mTOR
## ETP-46464 PI3K/Akt/mTOR
## CCT128930 PI3K/Akt/mTOR
## GSK2334470 PI3K/Akt/mTOR
## Nintedanib (BIBF 1120)_uncertain Protein Tyrosine Kinase
## WIKI4 Stem Cells & Wnt
## Axitinib Protein Tyrosine Kinase
## PHA-665752 Others
## PD173074 Angiogenesis
## AT13148 PI3K/Akt/mTOR
## AT7867 PI3K/Akt/mTOR
## Linifanib (ABT-869) Protein Tyrosine Kinase
## PD184352 (CI-1040) MAPK
## U0126-EtOH Others
## Dovitinib (TKI-258, CHIR-258) Angiogenesis
## Sunitinib Malate Microbiology
## Cediranib (AZD2171) Protein Tyrosine Kinase
## SGI-1776 free base JAK/STAT
## GSK2636771 PI3K/Akt/mTOR
## RepSox TGF-beta/Smad
## K02288 TGF-beta/Smad
## TWS119 PI3K/Akt/mTOR
## ICG-001 Stem Cells & Wnt
## PRI-724 Stem Cells & Wnt
## AZD4547 Angiogenesis
## CEP-33779 JAK/STAT
## YO-01027 Proteases
## WZ4003 PI3K/Akt/mTOR
## AVL-292 Angiogenesis
## ASP3026 Protein Tyrosine Kinase
## Entrectinib (RXDX-101) Protein Tyrosine Kinase
## BIO PI3K/Akt/mTOR
## Semaxanib (SU5416) Protein Tyrosine Kinase
## SU6656 Angiogenesis
## DCC-2036 (Rebastinib) Angiogenesis
## Purvalanol A Cell Cycle
## Dabrafenib (GSK2118436) MAPK
## PP1 Angiogenesis
## PP2 Angiogenesis
## WH-4-023 Angiogenesis
## Dasatinib Angiogenesis
## PD173955 Angiogenesis
## Ponatinib (AP24534) Angiogenesis
## PRT062607 (P505-15, BIIB057) HCl Angiogenesis
## LDN-214117 TGF-beta/Smad
## LGK-974 Stem Cells & Wnt
## AZ 960 JAK/STAT
## DASA-58 Others
## FH535 Stem Cells & Wnt
## Mubritinib (TAK 165) Protein Tyrosine Kinase
## VPS34-IN1 PI3K/Akt/mTOR
## IMD 0354 NF-_B
## Tyrphostin 9 Protein Tyrosine Kinase
## Pazopanib Protein Tyrosine Kinase
## Sorafenib MAPK
## SU11274 Neuronal Signaling
## AG-1024 Protein Tyrosine Kinase
## Amuvatinib (MP-470) Protein Tyrosine Kinase
## Sorafenib Tosylate Neuronal Signaling
## Foretinib (GSK1363089) Others
## SNS-314 Mesylate Others
## CYC116 Cell Cycle
## VX-680 (Tozasertib, MK-0457) Endocrinology & Hormones
## BX-912 PI3K/Akt/mTOR
## AZD1480 JAK/STAT
## Danusertib (PHA-739358) Others
## Quizartinib (AC220) Angiogenesis
## AEE788 (NVP-AEE788) Protein Tyrosine Kinase
## WZ3146 Protein Tyrosine Kinase
## ZM 447439 Others
## Barasertib (AZD1152-HQPA) Others
## PHA-680632 Cell Cycle
## Aurora A Inhibitor I Cell Cycle
## OSU-03012 (AR-12) Others
## MK-5108 (VX-689) Cell Cycle
## HTH-01-015 PI3K/Akt/mTOR
## MK-8745 Cell Cycle
## TPCA-1 NF-_B
## BMS-345541 NF-_B
## Dovitinib (TKI-258) Dilactic Acid Angiogenesis
## GSK1838705A Protein Tyrosine Kinase
## NVP-BSK805 2HCl JAK/STAT
## LY2784544 JAK/STAT
## SU9516 Cell Cycle
## CNX-2006 Protein Tyrosine Kinase
## Dacomitinib (PF299804, PF299) Protein Tyrosine Kinase
## JNK-IN-8 MAPK
## AZD9291 Protein Tyrosine Kinase
## GZD824 Angiogenesis
## ERK5-IN-1 MAPK
## XMD8-92 MAPK
## LY2835219 Cell Cycle
## CP-673451 Protein Tyrosine Kinase
## Crenolanib (CP-868596) Protein Tyrosine Kinase
## PHA-793887 Cell Cycle
## Ro 31-8220 Mesylate TGF-beta/Smad
## Ro3280 Cell Cycle
## LY2603618 Cell Cycle
## PF-477736 Cell Cycle
## TAK-632 MAPK
## LDC000067 Cell Cycle
## Fostamatinib (R788) Angiogenesis
## TAK-901 Cell Cycle
## Bosutinib (SKI-606) Angiogenesis
## AST-1306 Protein Tyrosine Kinase
## Imatinib Mesylate (STI571) Protein Tyrosine Kinase
## AZD8931 (Sapitinib) Protein Tyrosine Kinase
## Gefitinib (ZD1839) Protein Tyrosine Kinase
## Saracatinib (AZD0530) Angiogenesis
## OSI-420 Protein Tyrosine Kinase
## Pelitinib (EKB-569) Protein Tyrosine Kinase
## AZD3759 Protein Tyrosine Kinase
## PD168393 Protein Tyrosine Kinase
## AG-1478 (Tyrphostin AG-1478) Protein Tyrosine Kinase
## ZM 306416 Protein Tyrosine Kinase
## AC480 (BMS-599626) Neuronal Signaling
## Ibrutinib (PCI-32765) Angiogenesis
## ONO-4059 Angiogenesis
## LY2090314 PI3K/Akt/mTOR
## AZD2858 PI3K/Akt/mTOR
## CHIR-98014 PI3K/Akt/mTOR
## AZD1080 PI3K/Akt/mTOR
## CHIR-99021 (CT99021) HCl PI3K/Akt/mTOR
## Enzastaurin (LY317615) Neuronal Signaling
## 1-Azakenpaullone PI3K/Akt/mTOR
## CHIR-99021 (CT99021) PI3K/Akt/mTOR
## Sotrastaurin TGF-beta/Smad
## GF109203X TGF-beta/Smad
## Go 6983 TGF-beta/Smad
## AZ 628 MAPK
## MLN2480 MAPK
## CX-6258 HCl JAK/STAT
## Piceatannol Angiogenesis
## PQ 401 Protein Tyrosine Kinase
## Tyrphostin AG 879 Protein Tyrosine Kinase
## Targets
## Ulixertinib (BVD-523, VRT752271) erk
## TAK-733 mek
## Trametinib (GSK1120212) mek
## Cobimetinib (GDC-0973, RG7420) mek
## MEK162 (ARRY-162, ARRY-438162) mek
## PD0325901 mek
## Refametinib (RDEA119, Bay 86-9766) mek
## AZD8330 mek
## Pimasertib (AS-703026) mek
## RAF265 (CHIR-265) raf, vegfr
## Selumetinib (AZD6244) mek
## PD318088 mek
## VX-11e erk
## NVP-BHG712 vegfr, src, raf, bcr-abl
## YM201636 pi3k
## Lapatinib (GW-572016) Ditosylate egfr, her2
## PF-573228 fak
## BMS-265246 cdk
## KU-60019 atm
## Triciribine akt
## CUDC-101 hdac, egfr, her2
## GSK461364 plk
## AZD7762 chk
## Rigosertib (ON-01910) plk
## BI 2536 plk
## JNJ-7706621 cdk, aurora kinase
## PIK-93 pi3k, vegfr
## PF-4708671 s6 kinase
## Flavopiridol (Alvocidib) cdk
## AT7519 cdk
## SNS-032 (BMS-387032) cdk
## Staurosporine_500nM <NA>
## BMS-754807 igf-1r
## LY3023414 akt
## Hesperadin aurora kinase
## KW-2449 flt, bcr-abl, aurora kinase
## Crizotinib (PF-02341066) c-met, alk
## ENMD-2076 flt, aurora kinase, vegfr
## HMN-214 plk
## BKM120 (NVP-BKM120, Buparlisib) pi3k
## NVP-AEW541 igf-1r
## Alisertib (MLN8237) aurora kinase
## GSK2292767 pi3k
## BGT226 (NVP-BGT226) pi3k, mtor
## Bortezomib psmb1
## CUDC-907 hdac, pi3k
## CHIR-124 chk
## Pacritinib (SB1518) jak
## AT9283 bcr-abl, jak, aurora kinase
## Irinotecan / SN-38 top1mt
## JNK Inhibitor IX jnk
## Tivantinib (ARQ 197) c-met
## HS-173 pi3k
## Wnt agonist 1 wnt/beta-catenin
## FRAX597 pak
## Tepotinib (EMD 1214063) c-met
## Golvatinib (E7050) c-met, vegfr
## KX2-391 src
## VE-822 atm/atr
## AZ20 atm/atr
## AZD6738 atm/atr
## AMG-900 aurora kinase
## PF-543 sphk1
## PF-3758309 pak
## AZD5438 cdk
## R547 cdk
## A-674563 akt, cdk, pka
## Dinaciclib (SCH727965) cdk
## P276-00 cdk
## Flavopiridol HCl cdk
## PHA-767491 cdk
## PF-00562271 fak
## PF-562271 fak
## IKK-16 (IKK Inhibitor VII) ikk
## LDK378 alk
## Vacquinol-1 jnk
## GSK2126458 (GSK458) pi3k, mtor
## Torin 2 mtor
## AZD3463 alk
## AP26113 alk
## SC1 erk
## SGI-7079 vegfr
## PF-431396 fak
## TAE226 (NVP-TAE226) fak
## Milciclib (PHA-848125) cdk
## TG101209 flt, jak, c-ret
## TG101348 (SAR302503) jak
## GSK690693 akt
## GDC-0068 akt
## MK-2206 2HCl akt
## AZD5363 akt
## Uprosertib (GSK2141795) akt
## BMS-536924 igf-1r
## OSI-906 (Linsitinib) igf-1r
## ZSTK474 pi3k
## KU-0063794 mtor
## WYE-354 mtor
## Temsirolimus (CCI-779, NSC 683864) mtor
## Everolimus (RAD001) mtor
## Ridaforolimus (Deforolimus, MK-8669) mtor
## GDC-0980 (RG7422) mtor, pi3k
## INK 128 (MLN0128) mtor
## VS-5584 (SB2343) pi3k
## PF-04691502 mtor, pi3k, akt
## PP121 dna-pk, mtor, pdgf
## CC-223 mtor
## PP242 mtor
## AZD2014 mtor
## GDC-0349 mtor
## OSI-027 mtor
## AZD8055 mtor
## CH5132799 pi3k, mtor
## WYE-125132 (WYE-132) mtor
## CO-1686 (AVL-301) egfr
## A66 pi3k
## BYL719 pi3k
## Akti-1/2 akt
## WAY-600 mtor
## ETP-46464 mtor
## CCT128930 akt
## GSK2334470 pdk-1
## Nintedanib (BIBF 1120)_uncertain vegfr, pdgfr, fgfr
## WIKI4 wnt/beta-catenin
## Axitinib vegfr, pdgfr, c-kit
## PHA-665752 c-met
## PD173074 fgfr, vegfr
## AT13148 akt
## AT7867 akt, s6 kinase
## Linifanib (ABT-869) pdgfr, vegfr
## PD184352 (CI-1040) mek
## U0126-EtOH mek
## Dovitinib (TKI-258, CHIR-258) c-kit, fgfr, flt, vegfr, pdgfr
## Sunitinib Malate vegfr, pdgfr, c-kit, flt
## Cediranib (AZD2171) vegfr, flt
## SGI-1776 free base pim
## GSK2636771 pi3k
## RepSox tgf-beta/smad
## K02288 tgf-beta/smad
## TWS119 gsk-3
## ICG-001 wnt/beta-catenin
## PRI-724 wnt/beta-catenin
## AZD4547 fgfr
## CEP-33779 jak
## YO-01027 gamma-secretase
## WZ4003 ampk
## AVL-292 btk
## ASP3026 alk
## Entrectinib (RXDX-101) trk receptor
## BIO gsk-3
## Semaxanib (SU5416) vegfr
## SU6656 src
## DCC-2036 (Rebastinib) bcr-abl
## Purvalanol A cdk
## Dabrafenib (GSK2118436) raf
## PP1 src
## PP2 src
## WH-4-023 src
## Dasatinib src, bcr-abl, c-kit
## PD173955 bcr-abl
## Ponatinib (AP24534) bcr-abl, vegfr, fgfr, pdgfr, flt
## PRT062607 (P505-15, BIIB057) HCl syk
## LDN-214117 tgf-beta/smad
## LGK-974 wnt/beta-catenin
## AZ 960 jak
## DASA-58 pkm2
## FH535 wnt/beta-catenin
## Mubritinib (TAK 165) her2
## VPS34-IN1 pi3k
## IMD 0354 ikk
## Tyrphostin 9 egfr
## Pazopanib vegfr
## Sorafenib raf
## SU11274 c-met
## AG-1024 igf-1r
## Amuvatinib (MP-470) c-met, c-kit, pdgfr, flt, c-ret
## Sorafenib Tosylate vegfr, pdgfr, raf
## Foretinib (GSK1363089) c-met, vegfr
## SNS-314 Mesylate aurora kinase
## CYC116 aurora kinase, vegfr
## VX-680 (Tozasertib, MK-0457) aurora kinase
## BX-912 pdk-1
## AZD1480 jak
## Danusertib (PHA-739358) aurora kinase, fgfr, bcr-abl, c-ret, src
## Quizartinib (AC220) flt
## AEE788 (NVP-AEE788) egfr, flt, vegfr, her2
## WZ3146 egfr
## ZM 447439 aurora kinase
## Barasertib (AZD1152-HQPA) aurora kinase
## PHA-680632 aurora kinase
## Aurora A Inhibitor I aurora kinase
## OSU-03012 (AR-12) pdk-1
## MK-5108 (VX-689) aurora kinase
## HTH-01-015 ampk
## MK-8745 aurora kinase
## TPCA-1 ikk
## BMS-345541 i_b/ikk
## Dovitinib (TKI-258) Dilactic Acid flt, fgfr, pdgfr, vegfr, c-kit
## GSK1838705A igf-1, alk
## NVP-BSK805 2HCl jak
## LY2784544 jak
## SU9516 cdk
## CNX-2006 egfr
## Dacomitinib (PF299804, PF299) egfr
## JNK-IN-8 jnk
## AZD9291 egfr
## GZD824 bcr-abl
## ERK5-IN-1 erk
## XMD8-92 erk
## LY2835219 cdk
## CP-673451 pdgfr
## Crenolanib (CP-868596) pdgfr
## PHA-793887 cdk
## Ro 31-8220 Mesylate pkc
## Ro3280 plk
## LY2603618 chk
## PF-477736 chk
## TAK-632 raf
## LDC000067 cdk
## Fostamatinib (R788) syk
## TAK-901 aurora kinase
## Bosutinib (SKI-606) src
## AST-1306 egfr
## Imatinib Mesylate (STI571) pdgfr, c-kit, bcr-abl
## AZD8931 (Sapitinib) egfr, her2
## Gefitinib (ZD1839) egfr
## Saracatinib (AZD0530) src, bcr-abl
## OSI-420 egfr
## Pelitinib (EKB-569) egfr
## AZD3759 egfr
## PD168393 egfr
## AG-1478 (Tyrphostin AG-1478) egfr
## ZM 306416 vegfr
## AC480 (BMS-599626) her2
## Ibrutinib (PCI-32765) src
## ONO-4059 btk
## LY2090314 gsk-3
## AZD2858 gsk-3
## CHIR-98014 gsk-3
## AZD1080 gsk-3
## CHIR-99021 (CT99021) HCl pi3k/akt/mtor
## Enzastaurin (LY317615) pkc
## 1-Azakenpaullone gsk-3
## CHIR-99021 (CT99021) gsk-3
## Sotrastaurin pkc
## GF109203X pkc
## Go 6983 pkc
## AZ 628 raf
## MLN2480 raf
## CX-6258 HCl pim
## Piceatannol syk
## PQ 401 igf-1r
## Tyrphostin AG 879 her2
## EnrichedPathway EnrichedTarget
## Ulixertinib (BVD-523, VRT752271) MAPK <NA>
## TAK-733 MAPK mek
## Trametinib (GSK1120212) MAPK mek
## Cobimetinib (GDC-0973, RG7420) MAPK mek
## MEK162 (ARRY-162, ARRY-438162) MAPK mek
## PD0325901 <NA> mek
## Refametinib (RDEA119, Bay 86-9766) <NA> mek
## AZD8330 MAPK mek
## Pimasertib (AS-703026) MAPK mek
## RAF265 (CHIR-265) MAPK <NA>
## Selumetinib (AZD6244) MAPK mek
## PD318088 MAPK mek
## VX-11e MAPK <NA>
## NVP-BHG712 <NA> <NA>
## YM201636 <NA> <NA>
## Lapatinib (GW-572016) Ditosylate <NA> <NA>
## PF-573228 <NA> <NA>
## BMS-265246 <NA> <NA>
## KU-60019 <NA> <NA>
## Triciribine <NA> <NA>
## CUDC-101 <NA> <NA>
## GSK461364 Cell Cycle plk
## AZD7762 Cell Cycle <NA>
## Rigosertib (ON-01910) Cell Cycle plk
## BI 2536 <NA> plk
## JNJ-7706621 Cell Cycle <NA>
## PIK-93 <NA> <NA>
## PF-4708671 <NA> <NA>
## Flavopiridol (Alvocidib) Cell Cycle cdk
## AT7519 Cell Cycle cdk
## SNS-032 (BMS-387032) <NA> cdk
## Staurosporine_500nM <NA> <NA>
## BMS-754807 <NA> <NA>
## LY3023414 <NA> <NA>
## Hesperadin <NA> aurora kinase
## KW-2449 <NA> aurora kinase
## Crizotinib (PF-02341066) <NA> <NA>
## ENMD-2076 <NA> aurora kinase
## HMN-214 <NA> <NA>
## BKM120 (NVP-BKM120, Buparlisib) <NA> <NA>
## NVP-AEW541 <NA> <NA>
## Alisertib (MLN8237) <NA> aurora kinase
## GSK2292767 <NA> <NA>
## BGT226 (NVP-BGT226) <NA> <NA>
## Bortezomib <NA> <NA>
## CUDC-907 Cytoskeletal Signaling <NA>
## CHIR-124 <NA> <NA>
## Pacritinib (SB1518) <NA> jak
## AT9283 <NA> jak
## Irinotecan / SN-38 <NA> <NA>
## JNK Inhibitor IX <NA> <NA>
## Tivantinib (ARQ 197) <NA> c-met
## HS-173 <NA> <NA>
## Wnt agonist 1 <NA> <NA>
## FRAX597 Cytoskeletal Signaling <NA>
## Tepotinib (EMD 1214063) <NA> c-met
## Golvatinib (E7050) <NA> c-met
## KX2-391 <NA> <NA>
## VE-822 PI3K/Akt/mTOR atm/atr
## AZ20 PI3K/Akt/mTOR atm/atr
## AZD6738 PI3K/Akt/mTOR atm/atr
## AMG-900 <NA> <NA>
## PF-543 <NA> <NA>
## PF-3758309 Cytoskeletal Signaling <NA>
## AZD5438 Cell Cycle cdk
## R547 Cell Cycle cdk
## A-674563 <NA> cdk
## Dinaciclib (SCH727965) Cell Cycle cdk
## P276-00 Cell Cycle cdk
## Flavopiridol HCl Cell Cycle cdk
## PHA-767491 Cell Cycle cdk
## PF-00562271 <NA> fak
## PF-562271 <NA> fak
## IKK-16 (IKK Inhibitor VII) <NA> <NA>
## LDK378 <NA> alk
## Vacquinol-1 <NA> <NA>
## GSK2126458 (GSK458) <NA> <NA>
## Torin 2 <NA> <NA>
## AZD3463 <NA> alk
## AP26113 <NA> alk
## SC1 <NA> <NA>
## SGI-7079 <NA> <NA>
## PF-431396 <NA> fak
## TAE226 (NVP-TAE226) <NA> fak
## Milciclib (PHA-848125) <NA> <NA>
## TG101209 <NA> jak
## TG101348 (SAR302503) <NA> jak
## GSK690693 <NA> akt
## GDC-0068 <NA> akt
## MK-2206 2HCl <NA> akt
## AZD5363 <NA> akt
## Uprosertib (GSK2141795) <NA> akt
## BMS-536924 <NA> <NA>
## OSI-906 (Linsitinib) <NA> <NA>
## ZSTK474 <NA> <NA>
## KU-0063794 <NA> mtor
## WYE-354 <NA> mtor
## Temsirolimus (CCI-779, NSC 683864) <NA> mtor
## Everolimus (RAD001) <NA> mtor
## Ridaforolimus (Deforolimus, MK-8669) <NA> mtor
## GDC-0980 (RG7422) PI3K/Akt/mTOR mtor
## INK 128 (MLN0128) PI3K/Akt/mTOR mtor
## VS-5584 (SB2343) PI3K/Akt/mTOR pi3k
## PF-04691502 PI3K/Akt/mTOR mtor
## PP121 <NA> mtor
## CC-223 PI3K/Akt/mTOR mtor
## PP242 PI3K/Akt/mTOR mtor
## AZD2014 PI3K/Akt/mTOR mtor
## GDC-0349 PI3K/Akt/mTOR mtor
## OSI-027 PI3K/Akt/mTOR mtor
## AZD8055 PI3K/Akt/mTOR mtor
## CH5132799 PI3K/Akt/mTOR mtor
## WYE-125132 (WYE-132) PI3K/Akt/mTOR mtor
## CO-1686 (AVL-301) <NA> <NA>
## A66 PI3K/Akt/mTOR pi3k
## BYL719 PI3K/Akt/mTOR pi3k
## Akti-1/2 PI3K/Akt/mTOR <NA>
## WAY-600 PI3K/Akt/mTOR mtor
## ETP-46464 PI3K/Akt/mTOR mtor
## CCT128930 PI3K/Akt/mTOR <NA>
## GSK2334470 PI3K/Akt/mTOR <NA>
## Nintedanib (BIBF 1120)_uncertain <NA> vegfr
## WIKI4 Stem Cells & Wnt wnt/beta-catenin
## Axitinib <NA> vegfr
## PHA-665752 <NA> <NA>
## PD173074 <NA> vegfr
## AT13148 <NA> <NA>
## AT7867 <NA> <NA>
## Linifanib (ABT-869) <NA> vegfr
## PD184352 (CI-1040) <NA> <NA>
## U0126-EtOH <NA> <NA>
## Dovitinib (TKI-258, CHIR-258) <NA> vegfr
## Sunitinib Malate <NA> vegfr
## Cediranib (AZD2171) <NA> vegfr
## SGI-1776 free base <NA> <NA>
## GSK2636771 <NA> <NA>
## RepSox <NA> tgf-beta/smad
## K02288 <NA> tgf-beta/smad
## TWS119 <NA> <NA>
## ICG-001 Stem Cells & Wnt wnt/beta-catenin
## PRI-724 Stem Cells & Wnt wnt/beta-catenin
## AZD4547 <NA> fgfr
## CEP-33779 <NA> <NA>
## YO-01027 <NA> <NA>
## WZ4003 <NA> <NA>
## AVL-292 <NA> <NA>
## ASP3026 <NA> <NA>
## Entrectinib (RXDX-101) <NA> <NA>
## BIO <NA> <NA>
## Semaxanib (SU5416) <NA> <NA>
## SU6656 Angiogenesis src
## DCC-2036 (Rebastinib) Angiogenesis <NA>
## Purvalanol A <NA> <NA>
## Dabrafenib (GSK2118436) <NA> <NA>
## PP1 Angiogenesis src
## PP2 Angiogenesis src
## WH-4-023 Angiogenesis src
## Dasatinib Angiogenesis src
## PD173955 Angiogenesis bcr-abl
## Ponatinib (AP24534) Angiogenesis bcr-abl
## PRT062607 (P505-15, BIIB057) HCl Angiogenesis <NA>
## LDN-214117 <NA> tgf-beta/smad
## LGK-974 Stem Cells & Wnt wnt/beta-catenin
## AZ 960 <NA> <NA>
## DASA-58 <NA> <NA>
## FH535 Stem Cells & Wnt wnt/beta-catenin
## Mubritinib (TAK 165) <NA> <NA>
## VPS34-IN1 <NA> <NA>
## IMD 0354 <NA> <NA>
## Tyrphostin 9 <NA> <NA>
## Pazopanib <NA> <NA>
## Sorafenib <NA> <NA>
## SU11274 <NA> <NA>
## AG-1024 <NA> <NA>
## Amuvatinib (MP-470) <NA> <NA>
## Sorafenib Tosylate <NA> <NA>
## Foretinib (GSK1363089) <NA> <NA>
## SNS-314 Mesylate <NA> aurora kinase
## CYC116 <NA> aurora kinase
## VX-680 (Tozasertib, MK-0457) <NA> aurora kinase
## BX-912 <NA> <NA>
## AZD1480 <NA> <NA>
## Danusertib (PHA-739358) <NA> aurora kinase
## Quizartinib (AC220) <NA> <NA>
## AEE788 (NVP-AEE788) <NA> <NA>
## WZ3146 <NA> <NA>
## ZM 447439 <NA> aurora kinase
## Barasertib (AZD1152-HQPA) <NA> aurora kinase
## PHA-680632 <NA> aurora kinase
## Aurora A Inhibitor I <NA> aurora kinase
## OSU-03012 (AR-12) <NA> <NA>
## MK-5108 (VX-689) <NA> <NA>
## HTH-01-015 <NA> <NA>
## MK-8745 <NA> <NA>
## TPCA-1 <NA> <NA>
## BMS-345541 <NA> <NA>
## Dovitinib (TKI-258) Dilactic Acid <NA> <NA>
## GSK1838705A <NA> <NA>
## NVP-BSK805 2HCl <NA> <NA>
## LY2784544 <NA> <NA>
## SU9516 <NA> <NA>
## CNX-2006 Protein Tyrosine Kinase egfr
## Dacomitinib (PF299804, PF299) Protein Tyrosine Kinase egfr
## JNK-IN-8 <NA> <NA>
## AZD9291 Protein Tyrosine Kinase egfr
## GZD824 <NA> <NA>
## ERK5-IN-1 <NA> <NA>
## XMD8-92 <NA> <NA>
## LY2835219 Cell Cycle cdk
## CP-673451 <NA> <NA>
## Crenolanib (CP-868596) <NA> <NA>
## PHA-793887 Cell Cycle cdk
## Ro 31-8220 Mesylate <NA> <NA>
## Ro3280 Cell Cycle <NA>
## LY2603618 Cell Cycle <NA>
## PF-477736 Cell Cycle <NA>
## TAK-632 <NA> <NA>
## LDC000067 Cell Cycle cdk
## Fostamatinib (R788) <NA> <NA>
## TAK-901 Cell Cycle <NA>
## Bosutinib (SKI-606) <NA> src
## AST-1306 Protein Tyrosine Kinase egfr
## Imatinib Mesylate (STI571) Protein Tyrosine Kinase <NA>
## AZD8931 (Sapitinib) Protein Tyrosine Kinase egfr
## Gefitinib (ZD1839) Protein Tyrosine Kinase egfr
## Saracatinib (AZD0530) <NA> src
## OSI-420 Protein Tyrosine Kinase egfr
## Pelitinib (EKB-569) Protein Tyrosine Kinase egfr
## AZD3759 Protein Tyrosine Kinase egfr
## PD168393 Protein Tyrosine Kinase egfr
## AG-1478 (Tyrphostin AG-1478) Protein Tyrosine Kinase egfr
## ZM 306416 Protein Tyrosine Kinase <NA>
## AC480 (BMS-599626) <NA> her2
## Ibrutinib (PCI-32765) <NA> src
## ONO-4059 <NA> <NA>
## LY2090314 PI3K/Akt/mTOR gsk-3
## AZD2858 PI3K/Akt/mTOR gsk-3
## CHIR-98014 PI3K/Akt/mTOR gsk-3
## AZD1080 PI3K/Akt/mTOR gsk-3
## CHIR-99021 (CT99021) HCl PI3K/Akt/mTOR <NA>
## Enzastaurin (LY317615) <NA> <NA>
## 1-Azakenpaullone PI3K/Akt/mTOR gsk-3
## CHIR-99021 (CT99021) PI3K/Akt/mTOR gsk-3
## Sotrastaurin TGF-beta/Smad pkc
## GF109203X TGF-beta/Smad pkc
## Go 6983 TGF-beta/Smad pkc
## AZ 628 <NA> <NA>
## MLN2480 <NA> <NA>
## CX-6258 HCl <NA> <NA>
## Piceatannol <NA> <NA>
## PQ 401 <NA> <NA>
## Tyrphostin AG 879 <NA> her2
## Lethality
## Ulixertinib (BVD-523, VRT752271) 0
## TAK-733 6
## Trametinib (GSK1120212) 5
## Cobimetinib (GDC-0973, RG7420) 4
## MEK162 (ARRY-162, ARRY-438162) 2
## PD0325901 4
## Refametinib (RDEA119, Bay 86-9766) 4
## AZD8330 4
## Pimasertib (AS-703026) 3
## RAF265 (CHIR-265) 6
## Selumetinib (AZD6244) 2
## PD318088 2
## VX-11e 1
## NVP-BHG712 0
## YM201636 0
## Lapatinib (GW-572016) Ditosylate 0
## PF-573228 0
## BMS-265246 0
## KU-60019 0
## Triciribine 0
## CUDC-101 5
## GSK461364 0
## AZD7762 7
## Rigosertib (ON-01910) 5
## BI 2536 3
## JNJ-7706621 6
## PIK-93 0
## PF-4708671 0
## Flavopiridol (Alvocidib) 10
## AT7519 8
## SNS-032 (BMS-387032) 9
## Staurosporine_500nM 4
## BMS-754807 3
## LY3023414 2
## Hesperadin 3
## KW-2449 2
## Crizotinib (PF-02341066) 2
## ENMD-2076 1
## HMN-214 1
## BKM120 (NVP-BKM120, Buparlisib) 2
## NVP-AEW541 4
## Alisertib (MLN8237) 0
## GSK2292767 0
## BGT226 (NVP-BGT226) 12
## Bortezomib 14
## CUDC-907 12
## CHIR-124 9
## Pacritinib (SB1518) 14
## AT9283 10
## Irinotecan / SN-38 14
## JNK Inhibitor IX 0
## Tivantinib (ARQ 197) 1
## HS-173 0
## Wnt agonist 1 0
## FRAX597 0
## Tepotinib (EMD 1214063) 2
## Golvatinib (E7050) 3
## KX2-391 1
## VE-822 2
## AZ20 1
## AZD6738 2
## AMG-900 0
## PF-543 0
## PF-3758309 5
## AZD5438 9
## R547 10
## A-674563 11
## Dinaciclib (SCH727965) 9
## P276-00 10
## Flavopiridol HCl 8
## PHA-767491 5
## PF-00562271 5
## PF-562271 4
## IKK-16 (IKK Inhibitor VII) 4
## LDK378 4
## Vacquinol-1 2
## GSK2126458 (GSK458) 4
## Torin 2 6
## AZD3463 6
## AP26113 8
## SC1 5
## SGI-7079 3
## PF-431396 1
## TAE226 (NVP-TAE226) 2
## Milciclib (PHA-848125) 6
## TG101209 3
## TG101348 (SAR302503) 7
## GSK690693 0
## GDC-0068 0
## MK-2206 2HCl 0
## AZD5363 0
## Uprosertib (GSK2141795) 0
## BMS-536924 0
## OSI-906 (Linsitinib) 0
## ZSTK474 0
## KU-0063794 0
## WYE-354 0
## Temsirolimus (CCI-779, NSC 683864) 0
## Everolimus (RAD001) 0
## Ridaforolimus (Deforolimus, MK-8669) 0
## GDC-0980 (RG7422) 0
## INK 128 (MLN0128) 0
## VS-5584 (SB2343) 0
## PF-04691502 0
## PP121 7
## CC-223 0
## PP242 0
## AZD2014 0
## GDC-0349 0
## OSI-027 0
## AZD8055 0
## CH5132799 0
## WYE-125132 (WYE-132) 0
## CO-1686 (AVL-301) 0
## A66 0
## BYL719 0
## Akti-1/2 0
## WAY-600 0
## ETP-46464 0
## CCT128930 0
## GSK2334470 0
## Nintedanib (BIBF 1120)_uncertain 0
## WIKI4 0
## Axitinib 0
## PHA-665752 0
## PD173074 0
## AT13148 0
## AT7867 0
## Linifanib (ABT-869) 0
## PD184352 (CI-1040) 0
## U0126-EtOH 0
## Dovitinib (TKI-258, CHIR-258) 0
## Sunitinib Malate 0
## Cediranib (AZD2171) 0
## SGI-1776 free base 2
## GSK2636771 0
## RepSox 0
## K02288 0
## TWS119 0
## ICG-001 0
## PRI-724 0
## AZD4547 0
## CEP-33779 0
## YO-01027 0
## WZ4003 0
## AVL-292 0
## ASP3026 0
## Entrectinib (RXDX-101) 0
## BIO 0
## Semaxanib (SU5416) 0
## SU6656 0
## DCC-2036 (Rebastinib) 0
## Purvalanol A 0
## Dabrafenib (GSK2118436) 0
## PP1 0
## PP2 0
## WH-4-023 0
## Dasatinib 0
## PD173955 0
## Ponatinib (AP24534) 1
## PRT062607 (P505-15, BIIB057) HCl 0
## LDN-214117 0
## LGK-974 0
## AZ 960 1
## DASA-58 0
## FH535 0
## Mubritinib (TAK 165) 0
## VPS34-IN1 0
## IMD 0354 3
## Tyrphostin 9 0
## Pazopanib 0
## Sorafenib 0
## SU11274 0
## AG-1024 0
## Amuvatinib (MP-470) 0
## Sorafenib Tosylate 0
## Foretinib (GSK1363089) 0
## SNS-314 Mesylate 0
## CYC116 0
## VX-680 (Tozasertib, MK-0457) 0
## BX-912 0
## AZD1480 1
## Danusertib (PHA-739358) 0
## Quizartinib (AC220) 0
## AEE788 (NVP-AEE788) 1
## WZ3146 0
## ZM 447439 0
## Barasertib (AZD1152-HQPA) 0
## PHA-680632 0
## Aurora A Inhibitor I 0
## OSU-03012 (AR-12) 0
## MK-5108 (VX-689) 0
## HTH-01-015 0
## MK-8745 0
## TPCA-1 0
## BMS-345541 0
## Dovitinib (TKI-258) Dilactic Acid 0
## GSK1838705A 0
## NVP-BSK805 2HCl 0
## LY2784544 0
## SU9516 0
## CNX-2006 1
## Dacomitinib (PF299804, PF299) 1
## JNK-IN-8 1
## AZD9291 1
## GZD824 1
## ERK5-IN-1 0
## XMD8-92 0
## LY2835219 0
## CP-673451 0
## Crenolanib (CP-868596) 0
## PHA-793887 0
## Ro 31-8220 Mesylate 2
## Ro3280 0
## LY2603618 0
## PF-477736 1
## TAK-632 0
## LDC000067 0
## Fostamatinib (R788) 0
## TAK-901 1
## Bosutinib (SKI-606) 1
## AST-1306 0
## Imatinib Mesylate (STI571) 0
## AZD8931 (Sapitinib) 0
## Gefitinib (ZD1839) 0
## Saracatinib (AZD0530) 1
## OSI-420 1
## Pelitinib (EKB-569) 2
## AZD3759 0
## PD168393 0
## AG-1478 (Tyrphostin AG-1478) 0
## ZM 306416 0
## AC480 (BMS-599626) 0
## Ibrutinib (PCI-32765) 0
## ONO-4059 0
## LY2090314 0
## AZD2858 0
## CHIR-98014 0
## AZD1080 0
## CHIR-99021 (CT99021) HCl 0
## Enzastaurin (LY317615) 0
## 1-Azakenpaullone 0
## CHIR-99021 (CT99021) 0
## Sotrastaurin 0
## GF109203X 0
## Go 6983 0
## AZ 628 0
## MLN2480 0
## CX-6258 HCl 0
## Piceatannol 0
## PQ 401 0
## Tyrphostin AG 879 0Generate a force-directed graph from these similarities
# Set up adjacency matrix
adjmatrix = d
adjmatrix[adjmatrix > 45] = 0
# Resort matrix to make sure annotated nodes are above unannotated ones
sort_order = order(annotation$Target, na.last = FALSE)
adjmatrix = adjmatrix[sort_order, sort_order]
# Remove drugs without any connections
sel_indices = colSums(adjmatrix != 0) != 0
adjmatrix = adjmatrix[sel_indices, sel_indices]
# Convert to cosine distance: D = 1 - cos(angle)
# adjmatrix = 1 - cos(adjmatrix * pi / 180)
# Generate network
adjmatrix = Matrix::Matrix(data = adjmatrix, sparse = TRUE)
net = graph_from_adjacency_matrix(
adjmatrix = adjmatrix, mode = "undirected", diag = FALSE, weighted = TRUE)
# Assign targets and colors
V(net)$target = annotation[match(V(net)$name, rownames(annotation)), "Target"]
V(net)$color = V(net)$target
for(target in na.omit(unique(V(net)$target))) {
regex = paste0("\\b", target, "\\b")
V(net)$color = gsub(regex, anno_colorScale$Target[target], V(net)$color)}
V(net)$color[is.na(V(net)$color)] = "white"
# Assign size
V(net)$size = 5
V(net)[!is.na(V(net)$target)]$size = 7.5
# # Create plot
# e = get.edgelist(net, names = FALSE)
# l = qgraph.layout.fruchtermanreingold(
# edgelist = e, vcount = vcount(net),
# area=20*(vcount(net)^2), repulse.rad=(vcount(net)^3.1))
# # l = layout_with_graphopt(graph = net, niter = 1000, mass = 100)
# plot.igraph(net, vertex.label = NA, layout=l,
# edge.width = 0.5, edge.color = "lightgray")
# if(save_images) {
# pdf(file = file.path(img_out_dir, "EffectVectorNetwork_allLines.pdf"),
# width = 7, height = 7, useDingbats = FALSE)
# plot.igraph(net, vertex.label = NA, layout=l,
# edge.width = 0.5, edge.color = "lightgray")
# dev.off()}4 Clinical Cancer Panel
sel_indices = !is.na(drug_effect_metadata$Concentration)
profiles = drug_effect_profiles[sel_indices, ]
metadata = drug_effect_metadata[sel_indices, ]
# Sort for convenience
order_indices = order(rownames(profiles))
profiles = profiles[order_indices, ]
metadata = metadata[order_indices, ]4.1 Drugs Active at All Concentrations
sig_concentrations = aggregate(
x = metadata$AUC_Mean >= auc_thresh,
by = list("Drug" = metadata$Drug,
"Line" = metadata$Line),
FUN = sum)
sig_conc_table = as.data.frame(table(sig_concentrations$x))
sig_conc_table$TextLoc = sig_conc_table$Freq - 50
ggplot(data = sig_conc_table) +
geom_col(mapping = aes(x = Var1, y = Freq)) + theme_vignette() +
geom_text(mapping = aes(x = Var1, y = TextLoc, label = Freq),
color = "white", fontface = "bold", size = 3) +
xlab("Active Concentrations") + ylab("Number of Drugs") + coord_flip() +
theme(axis.line = element_blank(), axis.text.x = element_blank(),
axis.ticks = element_blank())
if(save_images) ggsave(
filename = file.path(img_out_dir, "Barplot_NumActiveConcentrations.pdf"),
width = 2.5, height = 3)
# The number of concentrations at which drugs should be active
conc_thresh = 5
sig_concentrations = sig_concentrations[
sig_concentrations$x >= conc_thresh, ]
# Create heatmap for each entry
for(ii in seq_len(nrow(sig_concentrations))) {
drug = sig_concentrations[ii, "Drug"]
line = sig_concentrations[ii, "Line"]
sel_indices = metadata$Line == line & metadata$Drug == drug
entry = profiles[sel_indices, ]
entry_md = metadata[sel_indices, ]
angles = get_angles(entry)
d = acos(angles)*180/pi
hc = hclust(as.dist(d), method = "ward.D2")
hm_colorscale = colorRampPalette(
rev(c("#f7f7f7", "#E0F3F8", "#91BFDB", "#4575B4")))(150)
pheatmap(
d, cluster_rows = hc, cluster_cols = hc, color = hm_colorscale,
display_numbers = round(d), cellwidth = 20, cellheight = 20,
labels_row = entry_md$Concentration, show_colnames = FALSE,
main = paste0(line, " - ", drug))
}
